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White Paper

Humility in AI: Building Trustworthy and Ethical AI Systems

AI is becoming ubiquitous. More and more critical decisions are automated through machine learning models, determining the future of a business or making life-altering decisions for real people. The number of critical touch points is growing exponentially with the adoption of AI.

But with the incredible pace of the modern world, AI systems continually face new data patterns, which make it challenging to return reliable predictions. This could mean a catastrophic failure by the system down the line, especially without proper guardrails. These failures can also significantly erode human trust in AI, rendering it ineffective for real-world applications in many industries.

With the rising stakes, AI systems must be built to be humble, just like humans. AI should know when it is not sure about the right answer to transfer the critical decision-making process back to people.

In this ebook, we explore the concept of humility in AI systems and how it can be applied to existing solutions to ensure their trustworthiness, ethicality, and reliability in a fast-changing world.

Download this ebook to learn:

  • The basic concepts behind humility in AI
  • What makes AI systems susceptible to performance and accuracy issues
  • How AI systems can exhibit humility
  • What it takes to develop a systemic, qualified, and actionable understanding of the potential areas for weakness in AI systems
  • How humility in AI systems impacts their decisions
  • Real-life examples of business problems and issues with the underlying data used for predictions that may benefit from a humility framework
  • How humble AI system can improve tactical and strategic decisions
  • What actions an automated system should perform when it’s not sure about its predictive output
  • How DataRobot tackles predictive uncertainty with its Humble AI capability
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
    Evariant
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